Stress Tests in fsaps



Yüklə 203 Kb.
tarix15.10.2018
ölçüsü203 Kb.
#74460



Overview

  • Stress tests in FSAPs

  • Stress tests in central banks & banking supervisory agencies

  • Selected methodological issues

  • Recent developments in stress test methodology



Stress Tests in FSAPs

  • Part I



Defining Stress Tests



Types of Stress Tests

  • By aggregation

  • Individual exposures

  • Individual institutions

  • System-wide

    • on bank by bank* data (“bottom up”)
    • on aggregate data (“top down”)


Recent Experience with Stress Tests



Stress Tests in the FSAP



Stress Tests as a Multi-Stage Process



Stress Tests vs. Other Analytical Tools



Other Analytical Methods Complement Stress Tests



Evolving Role of Stress Tests in FSAP



Stress Tests in Central Banks & Banking Supervisory Agencies

  • Part II



Role of Stress Tests

  • Help focus supervisory processes on a risk basis

  • Support macroprudential analysis at the central banks (including in financial stability reports)

  • Assess effects of prospective policy changes



Stress Tests Conducted by Central Banks & Banking Supervisors

  • Bank-by-bank stress tests conducted by supervisors

  • Financial system stability reports include or refer to aggregate stress tests

  • Range of approaches – examples:

    • Hungary (focus on sensitivity calculations)
    • Norway (focus on sources of credit risk)


Implementation Issues



Coverage - Institutions



Coverage - Exposures



Methodology



Methodology



Organization

  • How frequently?

    • Standard set of tests on quarterly basis, market risk/sensitivity analysis more frequently, elaborate analysis (e.g. contagion) less frequently
  • Run by whom?

  • Which software?

    • Many start with Excel and E-Views, then integrate with supervisory information systems
  • Presentation, dissemination of the results

    • By bank (supervision; links with EWS)
    • By peer groups (macroprudential surveillance)


Selected Methodological Issues

  • Part III



Selected Methodological Issues



Macro Scenarios for Stress Tests

  • Historical scenarios

    • e.g. the 1997 turbulence and subsequent slowdown in East Asia
  • Hypothetical scenarios

    • Recognizing the limitations of macro models, especially for large shocks, would it be possible to use the central bank’s existing macro model?
    • Stochastic simulations based on the model?
      • Scenario design: relative sizes of shocks to the risk factors
      • Assessing likelihood of the scenarios


Worst Case vs. Threshold Approach



Controversy on Probability



Controversy on Probability



Foreign Exchange Risk

  • The standard sensitivity analysis

  • Note: C=capital, ARW=risk-weighted assets, F=net open position, e=exchange rate

  • The impact on capital adequacy roughly equals the shock times the open position…



Foreign Exchange Risk

  • ... but stress test must reflect non-linearity arising from FX options

  • Off-balance sheet (OBS) positions, which include options, are not negligible in many countries.

  • Example



Indirect FX Risk

  • Nonperforming loans vs. exchange rate

  • Usually much more significant than the direct FX risk

  • The analysis requires

    • Regression of leverage vs. NPLs
    • Inclusion of stock and flow exposures in FX


Interest Rate Risk

  • Duration is the key indicator, because

  • This allows to express changes in capital adequacy ratio as

  • where



Interest Rate Risk—Issues

  • Adequacy of the available data, including

    • Do banks report residual maturity properly?
    • Does the indicator capture the whole balance sheet?
    • Are off-balance sheet contracts included?
  • Simplified method: residual maturity plus weigths proposed by Basel Committee

  • Nonlinearity (duration changes with large changes in interest rates)

  • NPV may differ from the regulatory capital

  • Correlation between risk-weighted assets and assets

  • Indirect interest rate risk (see under credit risk)



Credit Risk Modeling

  • The most significant source of risk

  • Also, the most in need of strengthening

    • Mechanical approaches
    • Approaches based on corporate sector data (leverage, interest coverage) & possibly household sector data
    • Approaches based on loan performance data (including the VAR model already estimated)


1. Mechanical Approaches

  • Assume an inflow of new NPLs

    • Function of existing NPLs, performing loans, or a weighted sum of the two
  • Assume ↑ provisions on existing NPLs

  • Credit expansion model: inflow of new loans, followed by credit migration to and within NPLs

  • Do the above by sectors (e.g. corporate & household)



2. Data on Borrowers

  • Leverage vs. NPLs (a possible model) *

  • Top-down calculations

  • * Notes: Based on an actual model used by IMF staff for cross-country panel data estimates. Npls – ratio of non-performing loans to total loans, lev – leverage ratio, rcc – real cost of capital, reer – real effective exchange rate, y-hat – real GDP growth rate, p-hat – inflation rate, m-hat – growth rate of M1, d-hat – growth rate of domestic credit, roe – corporate sector return on equity



2. Data on Borrowers

  • Logit model predicting individual bankruptcy probabilities as a function of age, size, industry characteristics & corporate soundness indicators (earnings, liquidity, financial strength)

  • Include interest and exchange rates on the right hand side (to capture the indirect risk)

  • Link to individual banks through their exposures to the various groups of companies

  • Predict bank potential losses (also taking into account collateral)



2. Data on Borrowers

  • A simpler approach: exposure variables

    • Net open FX position & ratio of FX income to FX costs (for indirect FX risk)
    • Interest coverage (for indirect interest risk)
  • If exposure variable exceeds an estimated (assumed) threshold, default rate rises

  • Similarly to previous approach, translate to bank losses (after collateral)



3. Loan Performance Data

  • Advantages

    • Also available for household sector (with rapid lending growth in many countries)
    • Should be more readily available than leverage
  • Disadvantage

    • Lagging indicators of asset quality


Introducing Contagion Risk

  • Need to compile data for the following matrix



Introducing Contagion Risk

  • Exposure = all uncollateralized lending (including both on- & off-balance sheet exposures)

  • Currently, only data on total exposure of a bank to interbank market are available

  • Two types of the contagion stress test

    • “Pure” contagion test: A “fraud” in a bank; impact on other banks through interbank exposures
    • “Macro” contagion test: Macro shocks are grossed-up to trigger failure of weakest bank; followed by interbank contagion


Introducing Contagion Risk

  • Implementation (example for 4 banks)



Introducing Contagion Risk

  • Aggregate stress test vs. interbank contagion stress test



Equity & Real Estate Price Risk

  • Equity price risk—similar to FX risk

    • Net open positions in equities
    • Need to include off-balance sheet exposures
  • Banks’ exposure to real estate price risk

    • Direct exposure (investment in real estate)
    • Credit exposure (developers etc.)
    • Degree of real estate collateralization
      • loan to value ratio
      • default probability (from credit risk stress test)


Concentration Risks (Credit)

  • Simple example: sensitivity analysis for large exposures

  • More sophisticated example

    • Run regressions for default probability on corporate data (company-by-company), with dummy variables for the sectors/regions
    • Ways to define default probability (actual default—run a logit regression; or set a threshold for interest coverage ratio)
    • For a set of a bank’s exposures to sectors/regions, calculate implied default probability


Liquidity Risk

  • Focus on bank liquidity stress tests

  • Results reported off-site, validate during on-site visits

  • Off-site cross-check (sensitivity analysis)

    • Overall risk: assume a % of deposits withdrawn (percentages determined based on past bank runs, vary for different maturities)
    • Concentration risk in deposits (same as above, but for a percentage of the largest deposits)


Recent Developments in Stress Test Methodology

  • Part IV



Bank Internal Stress Test Models

  • Two surveys of stress test practices in commercial banks (2000 & 2004)

  • More attention to bank internal stress tests in on-site visits

  • Consider issuing guidelines on stress tests in commercial banks?

  • Cross-check results of bank & supervisor stress tests



Stress Tests vs. Early Warning Systems

  • Consider designing an EWS system in the form of a statistical model of detection of bank failure/stress

  • Could be back tested against the ratings

  • BIS working paper on EWS for banking supervision



Cross-Market Contagion

  • Coverage of non-bank financial institutions in the framework for consolidated supervision

  • Contagion between banks and non-bank financial institutions—e.g. insurance companies

  • Credit derivatives



Further Reading

  • Blaschke et al., 2001, “ST of Financial Systems: An Overview of Issues, Methodologies, and FSAP Experiences,” IMF WP 01/88

    • www.imf.org/external/pubs/cat/longres.cfm?sk=15166.0
  • IMF & WB, 2003, “Analytical Tools of the FSAP”

    • www.imf.org/external/np/fsap/2003/022403a.pdf
  • Čihák, 2004, “ST: A Review of Key Concepts,” Czech National Bank technical note 2/2004

    • http://www.cnb.cz/en/pdf/IRPN_2_2004.pdf


Yüklə 203 Kb.

Dostları ilə paylaş:




Verilənlər bazası müəlliflik hüququ ilə müdafiə olunur ©genderi.org 2024
rəhbərliyinə müraciət

    Ana səhifə